Occupancy

About: Occupancy is a research topic. Over the lifetime, 2757 publications have been published within this topic receiving 68288 citations.

An integrated approach to occupancy modeling and estimation in commercial buildings

Abstract: The problem of real-time estimation of occupancy in a commercial building (number of people in various zones at every time instant) is relevant to a number of emerging applications, such as green buildings that achieve high energy efficiency through feedback control. Due to the high deployment cost and large errors that people counting sensors suffer from, measuring occupancy throughout the building accurately from sensors alone is not feasible. Fusing sensor data with model predictions is essential. Due to the highly uncertain nature of occupancy dynamics, modeling and estimation of occupancy is a challenging problem. This paper makes two contributions toward addressing these challenges. We develop an agent-based model to simulate the behavior of all the occupants of a building, and extract reduced-order graphical models from Monte-Carlo simulations of the agent-based model. The agent-based model is validated with sensor data for the special case of one room and one occupant. Noisy measurements from a few sensors are fused with the graphical model predictions using the classical LMV estimator to estimate room-level occupancy in the building. Simulations illustrate the effectiveness of the proposed method.

Household occupancy monitoring using electricity meters

Abstract: Occupancy monitoring (i.e. sensing whether a building or room is currently occupied) is required by many building automation systems. An automatic heating system may, for example, use occupancy data to regulate the indoor temperature. Occupancy data is often obtained through dedicated hardware such as passive infrared sensors and magnetic reed switches. In this paper, we derive occupancy information from electric load curves measured by off-the-shelf smart electricity meters. Using the publicly available ECO dataset, we show that supervised machine learning algorithms can extract occupancy information with an accuracy between 83% and 94%. To this end we use a comprehensive feature set containing 35 features. Thereby we found that the inclusion of features that capture changes in the activation state of appliances provides the best occupancy detection accuracy.

Building-level occupancy data to improve ARIMA-based electricity use forecasts

Abstract: The energy use of an office building is likely to correlate with the number of occupants, and thus knowing occupancy levels should improve energy use forecasts. To gather data related to total building occupancy, wireless sensors were installed in a three-storey building in eastern Ontario, Canada comprising laboratories and 81 individual work spaces. Contact closure sensors were placed on various doors, PIR motion sensors were placed in the main corridor on each floor, and a carbon-dioxide sensor was positioned in a circulation area. In addition, we collected data on the number of people who had logged in to the network on each day, network activity, electrical energy use (total building, and chilling plant only), and outdoor temperature. We developed an ARIMAX model to forecast the power demand of the building in which a measure of building occupancy was a significant independent variable and increased the model accuracy. The results are promising, and suggest that further work on a larger and more typical office building would be beneficial. If building operators have a tool that can accurately forecast the energy use of their building several hours ahead they can better respond to utility price signals, and play a fuller role in the coming Smart Grid.

Occupancy Modeling and Prediction for Building Energy Management

Abstract: Heating, cooling and ventilation accounts for 35p energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This article shows how real time occupancy data from a wireless sensor network can be used to create occupancy models, which in turn can be integrated into building conditioning system for usage-based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42p annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) comfort standards.